Last updated: 2021-12-23

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Introduction

Relative forest cover – exploratory

All observations

Outliers excluded

The following table displays the two outliers that will be excluded in the subsequent analyses:

wdpa_pid NAME ISO3 AREA_KM2
31758 Área De Proteção Ambiental Serra Da Tabatinga BRA 417.9696
30628 Río Indio Maíz NIC 3161.2738

Without the outliers, the y scale can be cut off at around 0.7 at the bottom and 1.3 at the upper end. That way, we can see what’s going on in most projects in more detail:

From this point onwards, all plots exclude aforementioned outliers.

Grouped by countries

Grouped by accessibililty

A first look at accessibility:

Version Author Date
d332a1b Yota Eilers 2021-12-07 
[1]  1881  1998 65535 65535 65535 65535

It seems like one PA has a wrong travel time: 65,535 mins = 1092.25h! This PA is located on an island. For now, we will drop the corresponding observations (one PA, 5 ‘projects’):

Version Author Date
d332a1b Yota Eilers 2021-12-07

Here, we set alternative cutoff vectors for travel time:

cutoffs_travel_time <- 
  c(0,120,300,max(wdpa_accessibility$travel_time_to_nearby_cities_min,na.rm = T))

cutoffs_travel_time_alt2 <- 
  c(0,120,240,300,max(wdpa_accessibility$travel_time_to_nearby_cities_min,na.rm = T))

cutoffs_travel_time_alt3 <- 
  c(0,60,120,300,max(wdpa_accessibility$travel_time_to_nearby_cities_min,na.rm = T))

cutoffs_travel_time_alt4 <- 
  c(0,30,60,300,max(wdpa_accessibility$travel_time_to_nearby_cities_min,na.rm = T))

Grouped by alternative 1:

Grouped by alternative 2:

Grouped by accessibility, country

Grouped by accessibility, COL

Grouped by size

cutoffs_size <-
  c(0,150,3000,max(wdpa_info$AREA_KM2,na.rm = T)) # these breaks should be revised
cutoffs_size_2 <- 
  quantile(inner_join(gfw_kfw_data, wdpa_info, by=c("wdpa_pid"))$AREA_KM2)


      (0.0758,115]          (115,682]     (682,2.74e+03] (2.74e+03,5.1e+04] 
              2247               2247               2247               2247

Grouped by disbursements

Warning: Removed 4 rows containing non-finite values (stat_bin).

cutoffs_disb <-
  c(0,100,1000,10000,max(finance_data$disb_yr_km2,na.rm = T)) # these breaks should be revised

Relative forest cover: Yearly averages

Let’s have a look at the number of observations for each relative year:

Relative forest cover: Fitted lines

In this section, we divide the observations into a pre- and post-treatment group:

  • Pre-treatment group: year of observation before or at project start
  • Post-treatment group: year of observation after project start (starting at year one)

We set these two parameters:

# set parameters
treatmentgap <- 1
pretrend <- 10
posttreat <- 12
ylower <- 0.97
yupper <- 1.03

Slope coefficients


(Slope) coefficients with break at relative year == 1
========================================================
                                 Dependent variable:    
                             ---------------------------
                                 area_pct_projstart     
--------------------------------------------------------
Dummy before vs. after start          -0.004***         
                                       (0.001)          
                                                        
Relative year                         -0.001***         
                                      (0.0001)          
                                                        
Relative year X Dummy                  0.0001           
                                      (0.0001)          
                                                        
Constant                              1.000***          
                                       (0.001)          
                                                        
--------------------------------------------------------
Observations                            9,009           
R2                                      0.188           
Adjusted R2                             0.187           
Residual Std. Error               0.023 (df = 9005)     
F Statistic                   693.886*** (df = 3; 9005) 
========================================================
Note:                        *p<0.1; **p<0.05; ***p<0.01

The regression output shown above displays the slope coefficients of the graphs in the following plots. Our variable of interest is Relative year X Dummy. A positive value indicates a reduction of forest cover losses, i.e. a smaller rate of forest cover decreases, after start of the project.

In our case, the slope coefficient is positive but small in size and statistically insignificant.

Fitted lines and scattered points

Version Author Date
d332a1b Yota Eilers 2021-12-07
1163f70 Yota Eilers 2021-11-25

Fitted lines and CIs only

Version Author Date
d332a1b Yota Eilers 2021-12-07
1163f70 Yota Eilers 2021-11-25
  • D: Difference in intercepts of the two regression lines
  • Note: all values (including the intercepts) are estimated! Thus, does not necessarily equal one, even though we know that all values at normal zero are equal to one. The intercept value depends on the value of the coefficient of relative year.
  • In fact, the intercept for pre-treatment is not estimated to be zero either (see parameters displayed in the plots)

Less pre-trend

Grouped by accessibility bins

The following plot divides our observations (protected PAs) into three ‘accessibility groups’, i.e. groups with similar travel time to the next city. This way, we can observe the relationship between forest cover loss dynamics (in general, and pre- and post-treatment trends more specifically) and accessibility of the PA. A change in the slope coefficient (within colored groups) allow statements about reductions or increases in forest cover loss dynamics.

Version Author Date
d332a1b Yota Eilers 2021-12-07
1163f70 Yota Eilers 2021-11-25

Description:

  • Flatter line (smaller absolute slope) in the post-treatment group, compared to pre-treatment
  • Strongest dynamic for protected areas with 2–6h travel time to the next city
  • Largest ‘reduction’ of forest cover losses after project start for protected areas with 2–6h travel time to the next city

Interpretation/ Discussion:

  • Are protected areas located 2–6h away from the next city the most threatened PAs?
  • PAs with higher accessibility (i.e. less travel time) may have a status as ‘local recreation area’, whereas forest areas with medium accessibility are at the current deforestation front.
  • Do we observe less forest cover losses in PAs close to cities because forest areas close to settlement areas have already had significant losses in the past (‘not much left to deforest’)?
  • Note: All these plots only include protected areas -> no statements about forest areas in general

Follow-up:

  • Should accessibility to the next city be normalized? Rationale: Substantial differences in country sizes
  • Is the deterioration of the trend in the groups with longer travel times driven by trends in a selected country?

Alternative accessibility bins

By using alternative ‘accessibility bins’, one can see heterogeneous forest cover loss dynamics within the 2–6h travel time group. A change in the slope coefficient (within colored groups) allow statements about reductions or increases in forest cover loss dynamics.

Description:

  • The 2–6h travel time group exhibits strongly hetergeneous forest cover loss dynamics.
  • Substantially higher forest cover losses in the 4–6h travel time group.
  • The reduction of losses in the 2–6h group (see plot above) is largely driven by reduced losses in the 2–4h group.
  • Losses in the 4–6h group have, on average, become larger since project start.

What about sample size in the subgroups?

The following table displays the number of observations for each travel time group (number of observations = number of data points; not number of ‘projects’). As indicated by the large confidence bands for the 4–6h group, the sample size in this group is rather small compared to the sample size in the other groups. Note, however, that the shown figures do not reflect the number of observations in each normalized year.


    [0,120]   (120,240]   (240,300] (300,2e+03] 
       5775        1260         231        1470

To do:

  • What are sensible cutoff points for the accessibility variable, considering differences between <5k and <20k data set?

Alternative accessibility bins #2

Version Author Date
d332a1b Yota Eilers 2021-12-07 

     [0,60]    (60,120]   (120,300] (300,2e+03] 
       4683        1092        1491        1470

Grouped by accessibility bins, selected country

Version Author Date
d332a1b Yota Eilers 2021-12-07

Grouped by accessibility bins, all but selected country

Grouped by size

Relative forest cover: Boxplots

Version Author Date
d332a1b Yota Eilers 2021-12-07
1163f70 Yota Eilers 2021-11-25 

R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.6 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1

locale:
 [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
 [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
 [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] ggpmisc_0.4.4   ggpp_0.4.2      stargazer_5.2.2 forcats_0.5.1  
 [5] stringr_1.4.0   dplyr_1.0.7     purrr_0.3.4     readr_1.4.0    
 [9] tidyr_1.1.4     tibble_3.1.6    tidyverse_1.3.1 plotly_4.9.3   
[13] ggplot2_3.3.4   sf_1.0-4       

loaded via a namespace (and not attached):
 [1] nlme_3.1-152       matrixStats_0.57.0 fs_1.5.0           lubridate_1.7.10  
 [5] httr_1.4.2         rprojroot_2.0.2    tools_3.6.3        backports_1.2.1   
 [9] bslib_0.2.5.1      utf8_1.2.2         R6_2.5.1           KernSmooth_2.23-20
[13] mgcv_1.8-36        DBI_1.1.1          lazyeval_0.2.2     colorspace_2.0-1  
[17] withr_2.4.2        tidyselect_1.1.1   compiler_3.6.3     git2r_0.28.0      
[21] cli_3.1.0          rvest_1.0.0        quantreg_5.86      SparseM_1.78      
[25] xml2_1.3.2         labeling_0.4.2     sass_0.4.0         scales_1.1.1      
[29] classInt_0.4-3     proxy_0.4-26       digest_0.6.27      rmarkdown_2.11    
[33] pkgconfig_2.0.3    htmltools_0.5.1.1  highr_0.8          dbplyr_2.1.1      
[37] htmlwidgets_1.5.3  rlang_0.4.12       readxl_1.3.1       rstudioapi_0.13   
[41] farver_2.1.0       jquerylib_0.1.4    generics_0.1.1     jsonlite_1.7.2    
[45] crosstalk_1.1.1    magrittr_2.0.1     polynom_1.4-0      Matrix_1.3-4      
[49] Rcpp_1.0.7         munsell_0.5.0      fansi_0.5.0        lifecycle_1.0.1   
[53] stringi_1.7.6      whisker_0.4        yaml_2.2.1         promises_1.2.0.1  
[57] crayon_1.4.2       lattice_0.20-44    splines_3.6.3      haven_2.3.1       
[61] hms_1.1.1          knitr_1.34         pillar_1.6.4       reprex_2.0.0      
[65] glue_1.5.1         evaluate_0.14      data.table_1.13.6  modelr_0.1.8      
[69] vctrs_0.3.8        httpuv_1.6.1       MatrixModels_0.4-1 cellranger_1.1.0  
[73] gtable_0.3.0       assertthat_0.2.1   xfun_0.24          broom_0.7.6       
[77] e1071_1.7-9        later_1.2.0        class_7.3-19       viridisLite_0.4.0 
[81] conquer_1.0.2      workflowr_1.6.2    units_0.7-2        ellipsis_0.3.2